Most genetic or mechanistic studies of Autism Spectrum Disorders (ASDs) still focus predominantly on protein- coding genes. The genomic landscape has, however, expanded greatly in recent years, with the identification of new transcript classes such as conserved long noncoding RNAs (lncRNAs) in humans and other eukaryotes that cover a significant fraction of the genome but so far remain largely uncharacterized. Though the functions of the vast majority of lncRNAs are unclear, they have been implicated in numerous gene transcription processes, as indicators of transcription factor activity, decoys that titrate away RNA binding proteins, functional guides for ribonucleoprotein complexes, and scaffolds for the assembly of functionally related proteins like transcriptional regulators. RNA coimmunoprecipitation experiments have also indicated that many are associated with chromatin-modifying complexes. As such, these transcripts are now believed to be a key component of gene regulatory networks. Crucially, there is emerging evidence that lncRNAs play a role in neurodevelopmental disorders, including ASDs, though systematic studies assessing their degree of involvement are still scarce. Comprehensive profiling of lncRNAs has remained challenging because they are typically expressed at much lower levels compared to messenger RNAs (mRNAs). We therefore intend to leverage newly developed hybrid short- and long-read sequencing and RNA capture technologies to deeply profile the lncRNA transcriptome in post-mortem brain samples of 40 ASD cases and 40 controls. Our efforts will be focused on two brain regions that have previously been implicated in ASD, the prefrontal cortex and the cerebellum. By deep short-read sequencing of samples enriched for lncRNAs using a specific set of capture probes we designed, we will identify lncRNAs that are dysregulated in ASD cases. The short-read data will further be combined with long-read sequencing data for a subset of samples generated on the PacBio RS platform to reconstruct full-length lncRNA transcript isoforms, which will provide a complete map of brain-expressed lncRNA genes and the diversity of transcripts they generate, in unprecedented detail. Finally, we will integrate our non-coding expression data with existing mRNA-Seq data generated for the same samples to construct coding/non-coding co-expression networks that can identify key driver lncRNAs whose dysregulation may contribute to ASD. Together these results will not only improve our understanding of the mechanisms underlying ASD pathogenesis, potentially providing new targets for prevention, earlier diagnostics and improved therapeutics, but also provide a more robust framework for future noncoding RNA studies in any disease context.